Robust Multiple Comparisons
| Autor: | James T. Ringland |
|---|---|
| Rok vydání: | 1983 |
| Předmět: | |
| Zdroj: | Journal of the American Statistical Association. 78:145-151 |
| ISSN: | 1537-274X 0162-1459 |
| Popis: | The first-order terms of the joint Edgeworth expansion for the p Studentized M estimates of location in a oneway layout are presented and the probabilities that generate the Bonferroni, maximum modulus, and Scheffe methods of multiple inference are approximated. The validity-robustness of these methods, based on both classical estimators and a variety of M estimators, will be compared for increasing numbers of simultaneous comparisons. The Bonferroni and maximum modulus methods are seen to be nonrobust when many comparisons are made. Scheffe's method is more stable. These results are fairly independent of the choice of estimator. Monte Carlo results indicate these expansion approximated probabilities accurately reflect qualitative behavior and give reasonable numerical values for moderately trimmed M estimators, but are insufficiently accurate to generate critical points for heavily trimmed estimators. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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